ADASI has adopted VentureOne's Perceptra, a GPS-less navigation technology, and Saluki, a high-security flight control technology, both developed by the Technology Innovation Institute (TII). These technologies enhance resilience, precision, and security for autonomous aerial operations, addressing vulnerabilities in GPS-dependent systems. The agreement was formalized at IDEX 2025. Why it matters: This deployment of advanced autonomous flight technologies in the UAE strengthens aviation security and positions the region as a leader in resilient, GPS-independent navigation solutions.
KAUST researchers are using AI to analyze satellite imagery for the automated detection of ancient stone structures in northwest Saudi Arabia, including mustatils (rectangular structures dating to the late 6th millennium BCE) and ruins in circular and triangular shapes. They developed a deep learning algorithm trained on manually identified datasets to isolate similar features over a wide area. The tool converts detected pixels into geodetic coordinates using GPS, assembling them into an online map and database. Why it matters: This project exemplifies computational archaeology, speeding up archaeological discoveries, promoting cultural heritage, and providing a methodology useful to other sectors of the economy.
The Secure Systems Research Center (SSRC) won the Best Paper Award at EWSN 2023 for "BLoB: Beating-based Localization for Single-antenna BLE Devices," which introduces a method using concurrent transmissions to localize Bluetooth tags accurately. The system achieves sub-meter accuracy in indoor environments by having multiple anchors transmit simultaneously. A second SSRC paper, "InSight: Enabling NLOS Classification...", was also a runner-up in the Best Paper category. Why it matters: This award highlights the growing research capabilities in IoT and localization technologies within the GCC region, particularly for indoor environments where GPS is unavailable.
KAUST researchers developed a low-cost, AI-powered drone system to recognize and track camels, addressing challenges faced by local herders. The system uses commercial drones, cameras, and machine learning to monitor camel herds in real time without expensive GPS collars. The AI model revealed insights into camel migration patterns, showing coordinated grazing and sensitivity to drone sounds. Why it matters: This system offers an affordable solution to preserve Saudi Arabia's camel herding tradition while providing valuable insights into camel behavior and contributing to the local economy.
KAUST researchers analyzed 17 years of GPS data from 168 stations across the Arabian plate. They found the plate to be remarkably stable despite pressure from continental collision and plate breakup. The plate moves as a single block, and its motion relative to neighboring plates has likely remained unchanged for 13 million years. Why it matters: The study provides crucial insights into earthquake hazards and tectonic activity in the Arabian Peninsula, improving risk assessment and infrastructure planning.
A Carnegie Mellon team (Tartan) presented their approach to rapidly deployable and robust autonomous aerial vehicles at the 2020 Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The system utilizes common techniques in vision and control, encoding robustness into mission structure through outcome monitoring and recovery strategies. Their system placed fourth in Challenge 2 and seventh in the Grand Challenge, with achievements in balloon popping, block manipulation, and autonomous firefighting. Why it matters: The work highlights strategies for building robust autonomous systems that can operate without central communication or high-precision GPS in challenging real-world environments, directly addressing key needs in the development of field robotics for the Middle East.
Researchers at MIT and QCRI developed Mapster, a human-in-the-loop street map editing system. Mapster incorporates high-precision automatic map inference, data refinement, and machine-assisted map editing. Evaluation across forty cities using satellite imagery, GPS trajectories, and ground-truth data demonstrates Mapster's ability to make automation practical for map editing. Why it matters: This system could significantly improve the accuracy and completeness of street maps in rapidly developing urban areas across the Middle East.
KAUST's Visual Computing Center (VCC) hosted an Open House event on March 28, showcasing its interdisciplinary research in visual computing. Demonstrations included a virtual reality driving simulator by FalconViz, intended for driver education in Saudi Arabia. Researchers also presented a drone trained to autonomously navigate race courses and a neural network for autonomous driving using image-based technology without GPS. Why it matters: The VCC's work highlights KAUST's role in advancing visual computing applications relevant to Saudi Arabia, from driver training to autonomous systems.